Remote surveillance of airborne and ground vehicles rely upon strategic or theatre-level systems (e.g., satellites, AWACS) for detecting and identifying targets by sensing radiated or reflected electromagnetic energy from target vehicles. Generally, these surveillance systems provide automatic target recognition (ATR) capabilities wherein the multi-spectral and spatial characteristics of each return can identify the type of vehicle. Enhanced range or surveillance or additional spectral bandwidth is often obtained by data fusion, using data from a number of different surveillance platforms.
Once one or more targets of interest have been located and identified, cooperative action may be directed against the targets. The location and characterization of the target of interest is handed off to a tactical platform (e.g., aircraft, missile, directed energy weapon) for action. These tactical platforms require a high-degree of knowledge of current and predicted future location of the target in order to engage. This resolution of tracking is not available from the surveillance platforms. Consequently, a tracking system (e.g., tracking radar system) rapidly scans a narrow aerial volume or surface area about the location received from the surveillance system. The repetition rate of sweeps produces frames of returns that have time difference between respective frames that is relatively short as compared to the mobility of the identified targets. Thus, linking returns between frames is a straightforward matter of linking the most closely spatially related returns between frames. Predicting future locations of the target is made by extrapolating the track of previous returns.
Even with rapid sweep repetitions, the amount of processing required is high. Attempts to increase the number of targets that may be tracked generally requires increasing the time difference between returns, and thus adding a degree of complexity to the predictions required to link returns. For instance, U.S. Pat. No. 5,537,119 to Poore, Jr. is an example of a predictive technique in tracking targets. In particular, Poore, Jr. relies upon an iterative Lagrangian Relaxation technique to link returns between each processed frame in order to assign a track respectively to each target.
While Poore, Jr. provides certain processing advantages over other known preditive techniques, Poore, Jr. shares the general limitation of other predictive techniques in that time differences between frames must still be relatively short as compared to the mobility of the targets. In addition, these predictive techniques presume that each target is present frame to frame in order to maintain a track. Often, returns for a target are not present in a given frame due to factors such as aspects of the target that present a small return, relative velocities, intervening obstructions or atmospheric interference, limitations of the targeting system, etc.
Consequently, a significant need exists for a system and method for tracking that allows larger time intervals between frames and that may more robustly handle tracking when returns are missing in certain frames.